SynthesisBMC medical imaging2023
Image-based AI diagnostic performance for fatty liver: a systematic review and meta-analysis.
Synthesis in BMC medical imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed, 2 syntheses or guidelines pooled it, 12 citations in OpenAlex.
- Physics-aware imaging AI for quantitative MASLD biomarker mapping: a systematic review of deep learning and radiomics across ultrasound, CT, and MRI.Abdominal radiology (New York) · 2026Pooled it
- Diagnostic accuracy of artificial intelligence models for imaging detection of hepatic steatosis through systematic review and meta analysis.Scientific reports · 2025Pooled it
- Non-invasive imaging diagnostic techniques in metabolic dysfunction-associated steatotic liver disease: a roadmap for clinicians.Hormones (Athens, Greece) · 2026Review
- Applications of Artificial Intelligence and Smart Devices in Metabolic Dysfunction-associated Steatotic Liver Disease.Journal of clinical and translational hepatology · 2026Article
- Applications of artificial intelligence in abdominal imaging.Abdominal radiology (New York) · 2025Review
- Genetic predeterminants and recent advancements in steatotic liver disease: A roadmap toward precision hepatology.World journal of hepatology · 2025Review
- Application of Steatotic Donor Livers in Liver Transplantation.The Korean journal of gastroenterology = Taehan Sohwagi Hakhoe chi · 2025Review
- Advancements in Artificial Intelligence-Enhanced Imaging Diagnostics for the Management of Liver Disease-Applications and Challenges in Personalized Care.Bioengineering (Basel, Switzerland) · 2024Review
- LiverColor: An Artificial Intelligence Platform for Liver Graft Assessment.Diagnostics (Basel, Switzerland) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundThe gold standard to diagnose fatty liver is pathology. Recently, image-based artificial intelligence (AI) has been found to have high diagnostic performance. We systematically reviewed studies of image-based AI in the diagnosis of fatty liver.
methodsWe searched the Cochrane Library, Pubmed, Embase and assessed the quality of included studies by QUADAS-AI. The pooled sensitivity, specificity, negative likelihood ratio (NLR), positive likelihood ratio (PLR), and diagnostic odds ratio (DOR) were calculated using a random effects model. Summary receiver operating characteristic curves (SROC) were generated to identify the diagnostic accuracy of AI models.
results15 studies were selected in our meta-analysis. Pooled sensitivity and specificity were 92% (95% CI: 90-93%) and 94% (95% CI: 93-96%), PLR and NLR were 12.67 (95% CI: 7.65-20.98) and 0.09 (95% CI: 0.06-0.13), DOR was 182.36 (95% CI: 94.85-350.61). After subgroup analysis by AI algorithm (conventional machine learning/deep learning), region, reference (US, MRI or pathology), imaging techniques (MRI or US) and transfer learning, the model also demonstrated acceptable diagnostic efficacy.
conclusionAI has satisfactory performance in the diagnosis of fatty liver by medical imaging. The integration of AI into imaging devices may produce effective diagnostic tools, but more high-quality studies are needed for further evaluation.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.